Deep Learning (CAS machine intelligence, 2019)
This course in deep learning focuses on practical aspects of deep learning. We therefore provide jupyter notebooks (complete list of notebooks used in the course).
For doing the handson part on your own computer you can either install anaconda (details and installation instruction) or use the provided a docker container (details and installation instruction).
You can also execute the notebooks for the handson part on the cloud using binder or open them in colab.
To easily follow the course please make sure that you are familiar with the some basic math and python skills.
Info for the projects
You can join together in small groups and choose a topic for your DL project. You should prepare a poster and a spotlight talk (5 minutes) which you will present on the last course day. To get some hints how to create a good poster you can check out the links that are provided in poster_guidelines.pdf
If you need free GPU resources, we might want to follow the instructions how to use google colab. Help for preparing a hdf5 file from your images you can be found in the example Notebook.
Examples for projects from the DL course 2018 and 2019 can be found here.
Other resources
We took inspiration (and sometimes slides / figures) from the following resources.

Deep Learning Book (DLBook) http://www.deeplearningbook.org/. This is a quite comprehensive book which goes far beyond the scope of this course.

Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/, has additional material and youtube videos of the lectures. While the focus is on computer vision, it also treats other topics such as optimization, backpropagation and RNNs. Lecture notes can be found at http://cs231n.github.io/.

More TensorFlow examples can be found at dl_tutorial

Another applied course in DL: TensorFlow and Deep Learning without a PhD
Dates
The course is split in 8 sessions, each 4 lectures long.
Day  Date  Time 

1  Tue Feb 19  13:3017:00 
2  Tue Feb 26  13:3017:00 
3  Tue March 05  13:3017:00 
4  Tue March 12  09:0012:30 
5  Tue March 19  09:0012:30 
6  Tue March 26  09:0012:30 
7  Tue April 02  09:0012:30 
8  Tue April 09  09:0012:30 
Syllabus (might change during course)
Day  Topic and slides  Additional Material  Exercises and homework 

1 
Deep learning basics slides_day1


2 
Gradient Descent and loss functions for classification slides_day2



3 
Going Deeper / Tricks of the trade slides_day2 slides_day3</a>



4 
Convolutional Neural Networks I
slides_day4(pdf)
slides_day4(ppt)



5 
Convolutional Neural Networks II slides_day5



6 
Modern CNN Architectures and Recurent Neural Networks slides_day6



7 



8 
Uncertainties in DL slides_day8

Tensorchiefs are Oliver Dürr, Beate Sick and Elvis Murina.